TY - GEN
T1 - Enabling Communication for Locked-in Syndrome Patients using Deep Learning and an Emoji-based Brain Computer Interface
AU - Comaniciu, Alexandra
AU - Najafizadeh, Laleh
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/20
Y1 - 2018/12/20
N2 - Locked-in syndrome describes a condition in which patients are incapable of speaking or moving, although they do retain their cognitive capabilities. In this paper, we propose a novel Brain Computer Interface design using a versatile emoji-based symbol display and a deep learning solution to enable these patients to communicate using recordings obtained through electroencephalography (EEG). EEG signals are converted into images representing their spatiotemporal characteristics. Images are then classified using a deep convolutional neural network (CNN) to recognize the intended emoji symbol. A prototype of the proposed system was tested on five healthy volunteers, showing significant improvement in the recognition rate when compared to the classic LDA classifier.
AB - Locked-in syndrome describes a condition in which patients are incapable of speaking or moving, although they do retain their cognitive capabilities. In this paper, we propose a novel Brain Computer Interface design using a versatile emoji-based symbol display and a deep learning solution to enable these patients to communicate using recordings obtained through electroencephalography (EEG). EEG signals are converted into images representing their spatiotemporal characteristics. Images are then classified using a deep convolutional neural network (CNN) to recognize the intended emoji symbol. A prototype of the proposed system was tested on five healthy volunteers, showing significant improvement in the recognition rate when compared to the classic LDA classifier.
KW - Brain Computer Interface
KW - Convolutional networks
KW - Deep learning
KW - EEG
KW - P300
UR - http://www.scopus.com/inward/record.url?scp=85060881040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060881040&partnerID=8YFLogxK
U2 - 10.1109/BIOCAS.2018.8584821
DO - 10.1109/BIOCAS.2018.8584821
M3 - Conference contribution
AN - SCOPUS:85060881040
T3 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
BT - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
Y2 - 17 October 2018 through 19 October 2018
ER -